As　an　essential　element　of　intelligent　transport　systems,　the　Internet　of　vehicles　(IoV)　has　brought　an　immersive　user　experience　recently.　Meanwhile,　the　emergence　of　mobile　edge　computing　(MEC)　has　enhanced　the　computational　capability　of　the　vehicle　which　reduces　task　processing　latency　and　energy　consumption　effectively　and　meets　the　quality　of　service　requirements　of　vehicle　users.　However,　there　are　still　some　problems　in　the　MEC-assisted　IoV　system　such　as　poor　connectivity　of　vehicle　networks　and　high　cost　of　traditional　infrastructure　deployment　and　maintenance.　Unmanned　aerial　vehicles　(UAVs)　equipped　with　MEC　servers　have　become　a　promising　approach　for　providing　communication　and　computing　services　to　mobile　vehicles　because　of　their　agility,　low　cost,　and　readily　to　deployment.　Hence,　in　this　article,　an　optimal　framework　for　the　UAV-assisted　MEC　system　for　the　IoV　to　minimize　the　average　system　cost　is　presented.　Through　joint　consideration　of　computational　offloading　decisions　and　computational　resource　allocation,　the　optimization　problem　of　our　proposed　architecture　is　presented　to　reduce　system　energy　consumption　and　delay.　For　purpose　of　tackling　this　issue,　the　original　non-convex　issue　is　converted　into　a　convex　issue　and　the　alternating　direction　method　of　multipliers-based　distributed　optimal　scheme　is　developed.　The　simulation　results　illustrate　that　the　presented　scheme　can　enhance　the　system　performance　dramatically　with　regard　to　other　schemes,　and　the　convergence　of　the　proposed　scheme　is　also　significant.　©　2023　IEEE.